GUIDED BY: C.VENKATESH PRESENTED BY: S.FAHIMUDDIN C.VAMSI KRISHNA ASST.PROFESSOR M.V.KRISHNA REDDY (DEPT.ECE)

OVERVIEW 1) Introduction 2) Fitting And Hitting 3) Morphological operations 4) Results 5) Conclusion

 Morphological image processing is an important tool in digital image processing.  The term morphology refers to the branch of biology that deals with the form and structure of animals and plants.  Morphological image processing is generally based on the analysis of two valued image in terms of certain predetermined geometric shape known as structuring element.  The morphological operations are applied on gray scale images in medical image analysis.  The morphological image processing can also be used to educate the patient, about his health condition.

 Mathematical morphology is a powerful methodology which was initiated in the late 1960s by G.Matheron and J.Serra at the Fontainebleau School of Mines in France.  Nowadays it offers many theoretic and algorithmic tools inspiring the development of research in the fields of signal processing, image processing, machine vision, and pattern recognition.

Fitting and hitting The structuring element is positioned at all possible locations in the binary image. It is compared with the corresponding neighborhood of pixels. The morphological operation resembles a ‘Binary’ correction. The operation is logical than arithmetic in nature. Operations are FIT and HIT

The structuring element is said to FIT the image if each of its pixel and corresponding image pixel are ‘1’. For the above example both S1,S2 fit the image at ‘A’. The structuring element pixels set to ‘0’ are ignored when testing for a FIT. S2 fits the image at ‘B’ and neither S1 nor S2 fits at ‘C’. FIT concept corresponding to intersection (AND) operation.

 The structuring element is said to hit the image if, for any of its pixels and corresponding image pixels are ‘1’.  Here also we ignore image pixels for which the corresponding structuring element pixels is ‘0’.  For the above example, S1 and S2 hit the image in neighborhood of ‘A’, ‘B’.  But at neighborhood ‘C’ only S1 hits the image.  HIT concept corresponding to Union(OR) operation.

 In the given image A,B,C are the three positions where the S1 and S2 structuring elements are positioned  S1=  S2=

The four most basic operations in mathematical morphology are dilation, erosion, opening and Closing : DilationErosion OpeningClosing

Dilation Dilation : “Grow image regions”. The growing depends on choice of structuring element. Dilation make an object by adding pixels around its edges. The dilation of an image ‘A’ by a structuring element ‘B’ is written as A  B 1 if ‘B’ hits ‘A’ G(x, y)= 0 otherwise The results of dilation are influenced not only by the size of structuring element but also its shape. It can be performed on both binary and grey tone images.

Dilation The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels grow in size while holes within those regions become smaller.

Erosion  Erosion : shrink image regions  The shrink depends on the choice of structuring element.  It makes an object smaller by removing or eroding away the pixels on its edges.  The erosion of an image ‘A’ by a structuring element ‘B’ is denoted by A Θ B.  G(x, y)= 1 if ‘B’ fits ‘A’ 0 otherwise  Erosion is the morphological operation it can be performed on both binary and grey images.

Erosion This erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels shrink in size, and holes within those areas become larger

Opening  Opening : structured removal of image region boundary pixels  It is obtained by combining erosion and dilation.  Opening “separates the objects”, generally smoothes the contour of an image.  The opening of an image ‘A’ by a structuring element ‘B’ is denoted as A ○ B A ○ B = (A Θ B)  B  Opening operation is obtained by doing dilation on eroded image.  Opening involves one or more erosions followed

Opening A binary image containing a mixture of circles and lines. Suppose that we want to separate out the circles from the lines, so that they can be counted. Opening with a disk shaped structuring element 11 pixels in diameter gives

Closing Closing : structured filling in of image region and boundary pixels. It is obtained by combining erosion and dilation. Closing join the objects, it tends to smooth sections of contours but, as opposed to opening. It eliminates the holes and fills gaps in the contour. The closing of an image ‘A’ by a structuring element ‘B is denoted as A ● B A ● B = (A  B) Θ B Closing is obtained by doing erosion on dilated image. Closing Involves one or more dilations followed by one erosion.

Closing An image containing large holes and small holes. If it is desired to remove the small holes while retaining the large holes, then we can simply perform a closing with a disk-shaped structuring element with a diameter larger than the smaller holes, but smaller than the large holes.

 The morphological image processing operations is use to analyze the medical images.  In this project the grey tone image of a patient suffering from tuberculosis is considered (a) Original image

 By performing dilation operation to original image the tiny white spots which are not visible in the original image are clearly visible in dilated image.  It is very useful in diagnosis of T.B in depth. (b)Dilated image

 By performing erosion operation to original image,the lung tissue which is in black color can be analyzed.  If any fibrosis occurs,the tissue appears to be partially white.  In the original image,as there is no defect, the same is reflected in the erosion. (c)Eroded image

o In opening we can obtain smooth contour of the image (d) Open image

In closing also we can obtained the smooth contour of the image (e) Close image

o The morphological operations can enhance the image and analyze the depth of the disease from obtained corrected x-Ray images. o Medical image analysis : detection of tuberculosis disease from x-ray images. o It is implemented in java, java platform provides a convenient representation for images. o The gray scale operations are performed using console programming and it is fully console based. o This produces an output image when input image and structuring element is given.

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